Architectures.    We examined various inductive (standard) pretrained architectures as baselines for TransBoost. Specifically, we used a variety of ResNet [1]} architectures: ResNet18, ResNet34, ResNet50, ResNet101 and ResNet152, which were pretrained using the standard PyTorch training recipe [2]}. We also considered ResNet50-StrikesBack [3]}, which is the ResNet50 architecture trained with Timm's [4]} advanced training recipe.
Additionally, we show results with more advanced architectures spanning various families: EfficientNetB0 [5]} and MobileNetV3-L [6]}, ConvNext-T [7]}, and the Transformer models ViT-S [8]} and Swin-T [9]}, which were pretrained with advanced procedures of Timm's library [4]} in accordance to their original papers.
